Point-of-Care Diagnostic Tests for Detecting SARS-CoV-2 Antibodies: A Systematic Review and Meta-Analysis of Real-World Data
point-of-care diagnostics, Rapid testing, real world data, systematic review and meta-analysis, SARS-CoV-2, COVID-19
SARS-CoV-2 is responsible for a highly contagious infection, known as COVID-19. SARS-CoV-2 was discovered in late December 2019 and, since then, has become a global pandemic. Timely and accurate COVID-19 laboratory testing is an essential step in the management of the COVID-19 outbreak. To date, assays based on the reverse-transcription polymerase chain reaction (RT-PCR) in respiratory samples are the gold standard for COVID-19 diagnosis. Unfortunately, RT-PCR has several practical limitations. Consequently, alternative diagnostic methods are urgently required, both for alleviating the pressure on laboratories and healthcare facilities and for expanding testing capacity to enable large-scale screening and ensure a timely therapeutic intervention. To date, few studies have been conducted concerning the potential utilization of rapid testing for COVID-19, with some conflicting results. Therefore, the present systematic review and meta-analysis was undertaken to explore the feasibility of rapid diagnostic tests in the management of the COVID-19 outbreak. Based on ten studies, we computed a pooled sensitivity of 64.8% (95%CI 54.5–74.0), and specificity of 98.0% (95%CI 95.8–99.0), with high heterogeneity and risk of reporting bias. We can conclude that: (1) rapid diagnostic tests for COVID-19 are necessary, but should be adequately sensitive and specific; (2) few studies have been carried out to date; (3) the studies included are characterized by low numbers and low sample power, and (4) in light of these results, the use of available tests is currently questionable for clinical purposes and cannot substitute other more reliable molecular tests, such as assays based on RT-PCR.
Jianhong Wu, Brandon Michael Henry, Carlo Signorelli, Nicola Luigi Bragazzi, Matteo Riccò, Pietro Ferraro, Giovanni Gualerzi, Natalia Valeryevna Pyatigorskaya, Younes Ben Said, Silvia Ranzieri, Elena Nevolina